Distributed Learning of Quantum State Tomography Robust to Readout Errors

• URL: http://arxiv.org/abs/2604.14428v1
• Date: Wed, 15 Apr 2026 21:22:08 GMT
• Title: Distributed Learning of Quantum State Tomography Robust to Readout Errors
• Abstract summary: This paper introduces a unified framework for joint regional quantum state tomography with readout errors.<n>A multiqubit system is partitioned in overlapping regions, each region is assigned to a local density operator and a local confusion matrix.<n>We show that joint estimation improves state recovery over fixed-readout reconstruction, recovers a substantial portion of oracle performance, and reveals a clear tradeoff between state estimation performance, communication, and computation.
• Score: 28.619101416652395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scalable estimation of quantum states with readout errors is a central challenge in large multiqubit systems. Existing overlapping-tomography methods improve scalability by working with local subsystems, but they usually assume known or separately calibrated measurements. At the same time, readout-estimation methods model measurement errors without enforcing consistency among overlapping regional states. In this context, the present paper introduces a unified framework for joint regional quantum state tomography with readout errors. A multiqubit system is partitioned in overlapping regions, each region is assigned to a local density operator and a local confusion matrix, and neighboring regions are coupled through reduced-state consistency on shared subsystems. This leads to a structured bilinear optimization problem. To solve it, a distributed alternating method is developed in which the state-update step is handled by the alternating direction method of multipliers (ADMM), while the confusion-matrix updates are carried out locally in parallel. Analytical guarantees are also established, including a sufficient condition for local identifiability, local quadratic growth of the population misfit, and convergence of the inner state-update procedure. Simulations on Ring, Ladder, Torus, and Hub graph geometries show that joint estimation improves state recovery over fixed-readout reconstruction, recovers a substantial portion of oracle performance, and reveals a clear tradeoff between state estimation performance, communication, and computation.

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